论文标题
从交谈到行动以及问责制：监视具有深度神经网络和主题建模的政策制定者的公众讨论
From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling
论文作者
论文摘要
数十年来，关于气候的研究已经达成了人们的共识，即人类活动改变了气候，我们目前正在陷入气候危机。尽管公众讨论和有关缓解气候变化的研究工作增加了，但潜在的解决方案不仅需要讨论，而且还需要有效地部署。为了防止管理不善并保持政策制定者对政府程序的透明度和信息程度已被证明至关重要。但是，目前，有关气候变化讨论的信息数量以及来源的范围使公众和民间社会越来越难以维持概述以使政客负责。 作为回应，我们提出了一个多源主题汇总系统（Mustas），该系统将政策制定者的语音和言论从几个公开可用来源进行处理为易于消化的主题摘要。 Mustas使用新颖的多源混合潜在迪里奇莱特分配来模拟各种文档的主题。该主题摘要将为公众和公民社会提供评估，以评估政客在何处，如何以及何时谈论气候和气候政策，使他们能够使政客对减轻气候变化和缺乏的行动负责。
Decades of research on climate have provided a consensus that human activity has changed the climate and we are currently heading into a climate crisis. While public discussion and research efforts on climate change mitigation have increased, potential solutions need to not only be discussed but also effectively deployed. For preventing mismanagement and holding policy makers accountable, transparency and degree of information about government processes have been shown to be crucial. However, currently the quantity of information about climate change discussions and the range of sources make it increasingly difficult for the public and civil society to maintain an overview to hold politicians accountable. In response, we propose a multi-source topic aggregation system (MuSTAS) which processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. MuSTAS uses novel multi-source hybrid latent Dirichlet allocation to model topics from a variety of documents. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies, enabling them to hold politicians accountable for their actions to mitigate climate change and lack thereof.
